1. The document analyzes the relationship between experimental and research expenses of Japanese companies and various financial metrics like gross profit rate, total assets, and number of employees. 2. It develops two regression models - a simple one using 3 variables and an improved one using 11 variables. Both models show a significant relationship between experimental expenses and the included variables. 3. High experimental spending is correlated with characteristics like high profit rates, large total assets, cash flows, employee numbers, and presence of global companies. The document concludes the analysis supports the initial hypotheses.

1. 20/10/2012
Data Analysis 1
- The Prediction of Data Data Analysis Team
05
1
Nobuya Yoshizawa, Goshi Fujimoto, Atsuko Chiba, Xu Changjing

2. Outline
1. Objectives
2. Hypothesis
3. Analysis process
4. Result
5. Conclusion
6. Possible reasons
7. Role of members
Q&A
2

3. 1. Objectives
 Does the future investment cause the high
performance of management?
 What is Experimental and research expense?
The special expense for studying and researching
new product or new technology
Experimental and Research Expense (KYen/Firm)
⇒ Future
3
investment!
20,000 40,581
18,000
16,000
14,000
12,000
10,000
11,497
11,203
8,414
8,000
6,000
4,000
2,000
0
3,319
2,3692,1301,991
1,6281,3581,1991,1471,028
829 434 384

4. 2. Hypothesis
When Experimental and research expense is high,
Gross profit rate is high
When a company produces new products, they might be expensive in short
range and cause profitable.
Total Asset is high
Since a company produces new products and technology, the total asset of
the company must be high.
# of employee is high
Large manufacturing company in Japan has a lot of employees and owns the
laboratory to produce new products and technology.
4

5. 2. Hypothesis
 Scatter with E&R expense
Clear relationship between E&R expense and hypothesis variables.
We are going to make the multi-regression model next…
5

6. 3. Analysis
 To know deeply the objective data and
find the correlation with various data
6
1.
Overview
ing the
objectiv
e data
2. Making
the
correlatio
n matrix
3. Picking
up
explanatory
variables
4.
Developing
the multi
regression
model
5.
Improving
the multi
regression
model

7. 3-1. Overviewing the objective data
The overview of E&R expense
1. Half of firms with no investment to E&R
2. Another half of firms with wide range of investment
to E&R
7

8. 3-1. Overviewing the objective data
815 companies
(E&R expense > 0)
1275 companies
(E&R expense = 0)
1. We are just interested in those companies which have experimental
and research expense. So we decided to take the objective data of
815 out of 2090 companies.
2. We converted E&R expense to log10(E&R expense) as the
objective variable to adjust the wide range numerically.
8

9. 3-2. Making the correlation matrix
TotalA
sset
TotalAsset
logTotal
Asset
Current
Asset
LongTerm LongTermL logE&R
Asset
iability
expense
…
1
0.589
0.603
0.960
0.936
0.426 …
logTotalAsset
0.589
1
0.637
0.466
0.428
0.777
CurrentAsset
0.603
0.637
1
0.354
0.311
0.529 …
LongTermAsset
0.960
0.466
0.354
1
0.987
0.313 …
LongTermLiability
0.937
0.428
0.311
0.987
1
0.279 …
logE&Rexpense
0.426
0.777
0.529
0.313
…
…
…
…
0.279
…
1 …
…
To find the explanatory variables which have the strong
relationship with E&R expense.
To categorize the similar explanatory variables not to include
multicollinearity.
9
…

10. 3-3. Picking up explanatory variables
 Top variables which have strong relationship with
E&R expense
Log Total
Asset
Log Current Asset
0.777
Log
Depreciation
0.760
Log Personal
Expense
0.741
10
0.766
Log Number of
Employee
Log Note And
Account Payable
0.706
Log Sales Income
0.756
0.748
Log Aggregate Value Log
of Listed Stock
BreakEvenPoint
0.787
0.697

11. 3-4. Developing the multi regression
model
 Based on hypothesis and statistical approach, we
developed the multi regression model
 Hypothesis is the most important because model
must be easy to explain and be accepted to
audience.
 Then we tried to find the optimal explanatory
variables without decreasing t-value and R^2
Hypothesis
Statistics
A variable
E variable
B variable
C variable
D variable
11
Objective
variable
F variable
.
.
.
.

12. 3-5. Improving the multi regression
model
 An example for improvement
 We have found the relationship with
 Total asset: High negative correlation
 Current asset: High positive correlation
 Then we convert total asset to current asset ratio
(=Current asset / Total asset) to total asset as a very high
positive correlation
Current asset ratio is more important than total asset to
explain E&R expense because
• E&R expense is counted as deferred current asset
• Companies are more active than them with no E&R
12

13. 4. Result
Normalized
coefficient
P-value
Gross profit rate
0.258
P<0.001
Current asset ratio to total asset
0.106
P<0.001
Log Number of employee
0.090
P<0.05
Log Inventory product
0.076
P<0.001
Percentage of export
0.088
P<0.001
Average salary
0.188
P<0.001
Consolidated income ratio to single income
0.092
P<0.001
Investment security
0.073
p<0.01
-0.139
P<0.001
Log Note and account receivable
0.111
P<0.01
Log Depreciation
0.489
P<0.001
Personal expense
13

14. 5. Conclusion I
Smaller
residuals
Strongly fitted
R^2 = 0.5000
model based on hypothesis
R^2 = 0.750
Improved model
Common characteristics :
•
•
•
•
High profit rate, total asset ,cash flow and
High investment on experimental installations and
High number of employees and salary and,
Large global companies.
14

15. 5. Conclusion II
 As a result, we verified three hypothesis data and
one optimal data induced by improving multi
regression model. (Refer to Slide 11)
Correlation
The experimental and research expense is high
Gross profit
rate
Verified
The Capital Stock is correlated
Total asset
Verified
The total asset is correlated
# of employee
is high
Verified
The # of employee is correlated
Current asset
ratio
Verified
The current asset ratio is correlated.
15

16. 6. Possible reasons
 IT bubble era in 1996
-NEC, Fujitsu spent Experimental and research
expenses in 1996.
-IT bubble era, IT companies invested to market
research and advanced technology to identify
themselves from their domestic and foreign competitors.
 Japanese manufacturing style
-Large company, such as electricity, gas or exporting
firms were afford to have laboratory, and spend the
experimental and research expense.
16

17. 7. Role of members
Name
Role
Fujimoto Goshi
(Leader)
-Facilitator
-Analyzing data
Xu Changjing
(Co-leader)
-Analyzing data
Chiba Atsuko
-Analyzing data
Yoshizawa Nobuya
-Preparing presentation slide
17

18. Thank you for your attention.
Q&A
18

19. Appendix – simple model
lm(formula = logExperimentalAndResearchExpense ~
logNumberOfEmployee +
logTotalAsset + GrossProfitRate)
Residuals:
Min
1Q Median
3Q
Max
-2.02654 -0.27310 0.09164 0.37059 1.13517
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept)
-2.674841 0.158979 -16.825 < 2e-16 ***
logNumberOfEmployee 0.544842 0.082013 6.643 5.62e-11 ***
logTotalAsset
0.708790 0.070862 10.002 < 2e-16 ***
GrossProfitRate
0.014168 0.001247 11.365 < 2e-16 ***
--Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.4997 on 811 degrees of freedom
Multiple R-squared: 0.6715,
Adjusted R-squared: 0.6703
F-statistic: 552.5 on 3 and 811 DF, p-value: < 2.2e-16
19

20. Appendix – improved model
Residuals:
Min
1Q Median
3Q
Max
-2.02046 -0.23227 0.07728 0.29399 1.24620
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept)
-2.301e+00 1.676e-01 -13.731 < 2e-16 ***
logNumberOfEmployee
1.553e-01 7.990e-02 1.944 0.052296 .
logNoteAndAccountReceivabe
1.676e-01 5.764e-02 2.908 0.003743 **
logInventoryProduct
6.072e-02 1.624e-02 3.739 0.000198 ***
logDeprecoation
6.306e-01 6.513e-02 9.682 < 2e-16 ***
GrossProfitRate
1.588e-02 1.145e-03 13.873 < 2e-16 ***
PerCapitaPersonnelExpenseKYen
-7.497e-05 1.165e-05 -6.437 2.09e-10 ***
RatioTotalCurrentAsset
6.073e-01 1.535e-01 3.957 8.25e-05 ***
PercentageOfExport
4.642e-03 9.954e-04 4.663 3.65e-06 ***
ConsolidatedIncomeToSingleIncomeRatio 1.598e-01 3.371e-02 4.740 2.53e-06 ***
AverageSalary
3.255e-06 3.971e-07 8.197 9.72e-16 ***
InvestmentSecurity
3.755e-06 1.172e-06 3.204 0.001409 **
Residual standard error: 0.4382 on 803 degrees of freedom
Multiple R-squared: 0.7499,
Adjusted R-squared: 0.7465
F-statistic: 218.9 on 11 and 803 DF, p-value: < 2.2e-16
20